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At least 505 records · Page 28

Modeling Cyber Supply Chain Incidents with Multilayered Graph Motifs

As noted within the literature, supply chain includes people and organizations---manufacturers, integrators, and third-party vendors---that are involved in one or more stages of a product lifecycle. Since supply chains, by definition, include organizations and people, supply chain risk management activities must consider dependencies between an organization's business processes and third-party resources. Just as adversarial tactics can be implemented via techniques implemented via networked computer systems, so can such tactics be expressed via legal business relationships. A cyber incident may have an exponential impact downstream, for example, by leveraging a product's distribution channel (e.g. malicious updates in SolarWinds, buggy updates in CrowdStrike). Similarly, legitimate and legal business relationships also affect the attack surface exposure of systems, enabling long-term persistence and/or unknown impacts to product quality that are hard to detect. This paper catalogs several recent digital supply chain incidents and applies a multilayered network formalism to develop structural indicators (graph motifs) that reflect potentially-adversarial behavior. Finally, we compare and contrast the characteristics of adversarial tactics (e.g. Loss of Availability, Data Collection) that leverage cyber-physical dependencies to those that leverage legal organizational relationships.

97 - MATHEMATICS AND COMPUTING↗

Cyber-Informed Engineering (CIE) Guide for States

The Cyber-Informed Engineering (CIE) Guide for States provides state energy offices, public utility commissions, and partner organizations with a structured framework for integrating cyber-resilient engineering practices into energy planning, grantmaking, interconnection processes, and workforce development. As grid digitalization and the adoption of distributed energy resources accelerate, states face expanding cyber-physical risks that traditional cybersecurity measures alone cannot fully address. CIE offers a proactive, consequence-focused engineering methodology that emphasizes eliminating or mitigating high-impact failure modes through design, physical controls, and operational safeguards. The guide outlines the 12 core CIE principles, demonstrates their application through state-focused use cases—including grant evaluation rubrics, interconnection reviews, allow-list development, and training programs—and provides practical tools such as scoring frameworks, impact assessment methods, and implementation checklists. It also highlights pathways for state–utility collaboration and opportunities for technical assistance from national laboratories. By adopting CIE, states can enhance grid reliability, reduce lifecycle costs, strengthen supply-chain assurance, and foster a security-aware engineering culture that aligns with broader resilience and modernization goals. November 2025

29 - ENERGY PLANNING, POLICY AND ECONOMY↗

INTEGRATION OF DATA ANALYTICS WITH SYSTEM HEALTH PROGRAMS

Industry equipment reliability and asset management programs are essential elements that help ensure the safe and economical operation of nuclear power plants. The effectiveness of these programs is addressed in several industry developed and regulatory programs. However, these programs have proven to be labor intensive and expensive. There is an opportunity to significantly enhance the collection, analysis, and use of this information to provide more cost-effective plant operation. Additionally, there is an acute industry need to leverage advanced technology to reduce costs and improve operational effectiveness. The goal of this paper is to provide effective and efficient analytical methods and tools to support risk-informed decisions for the equipment reliability and asset management programs at nuclear power plants. This is accomplished by creating a direct bridge between component health/lifecycle data and decision making (e.g., maintenance scheduling and project prioritization). Here we are supporting typical system engineer decisions regarding maintenance activity scheduling and component ageing management. This is performed in a risk-informed context where herein the term “risk” is broadly constructed to include both plant reliability and economics. This framework combines data analytics tools to analyze equipment reliability data with risk-informed methods designed to support system engineer decisions (e.g., maintenance and replacement schedules, optimal maintenance posture) in a customizable workflow. A challenge is that the structure of this workflow strongly depends on the decision that needs to be made, the type of data available, and the constraints that need to be considered. Current methods are designed to provide specific answers to specific problems; however, these methods might prove to be inadequate even when problem settings slightly change (e.g., different types of requirements, additional dependencies between system reliability and economics). We tackled this challenge by designing framework in a flexible and modular fashion such that the user can assemble and customize his/her own workflow that integrates SSC economic lifecycle models (e.g., maintenance and replacement costs), system reliability models, and optimization methods.

97 - MATHEMATICS AND COMPUTING↗

Progress on the MARVEL Cybersecurity by Design Model-Based Systems Engineering Project

Formal model-based systems engineering (MBSE) combines a model, systems thinking, and systems engineering to visually depict the boundaries, context, and behavior of interconnected systems, facilitating effective design, development, and utilization of engineered systems throughout the systems engineering lifecycle. Although nuclear reactor vendors employ these tools to integrate functionality, performance, and safety, they are not yet addressing digital risk concerns introduced by use of operational technology, such as digital instrumentation and control systems. To accomplish this objective, the Microreactor Applications Research Validation and EvaLuation (MARVEL) microreactor was used as an MBSE case study. This real-world application provides a first-of-a-kind opportunity to demonstrate the benefits of integrating digital risk and cybersecurity into the MBSE design process of a nuclear reactor. This paper provides an update of the ongoing MARVEL Cyber MBSE project as it specifically relates to the integration of digital risk management and cybersecurity by design.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

Accelerating Surface Radiation Content to Investigate the Impact of Radon Progeny on Superconducting Qubits

Ionizing radiation in the form of $α$, $β$, $γ$, and additional high-energy particles can induce decoherence via phonon and quasiparticle poisoning in superconducting qubits. Recent studies have explored this effect using cosmic rays or controlled radioactive sources held in the proximity of a qubit package, and have concluded that reductions in such ``external'' environmental radiation may benefit stable operation of qubit devices. However, the effect of long-lived, unstable daughters of $^{222}$Rn that ``plate out'' directly on device and packaging surfaces has not been as extensively explored. This plate-out process, well-known to the dark matter direct detection field, occurs throughout the fabrication and testing lifecycle of a device and (separately) its packaging, and produces a local source of $α$-decays which can remain active for decades. As this scales with chip area, understanding and managing this source of ionizing radiation is relevant for successfully scaling quantum computing architectures to larger numbers of qubits in a radiation-robust way. We present a setup capable of accelerating and enhancing radon daughter plateout by a factor of $7\times10^4$ over ambient, in order to study, \textit{in situ}, the impact of these events on superconducting qubits. We also provide outlook on the potential impact of this source of ionizing radiation on current and future qubit arrays.

Poudel, Sagar S. [South Dakota Sch. Mines Tech.]↗

First-Principles Cost Analysis of Advanced High-Temperature Nuclear Plants

Due to the vast number of recent nuclear reactor innovations, particularly those pertaining to generation IV reactor types like high-temperature gas cooled reactors (HTGRs) and sodium-cooled fast reactors (SFRs), and the newer deployment strategies envisioned, such as use of small modular reactors (SMRs) or even microreactors, reliable, detailed, and complete costs of these nuclear innovations are needed in wide availability. The types of models that generally achieve these objectives are those incorporating the fundamental nature of the real-world systems they aspire to predict, such as first-principles models. Furthermore, first principles models typically offer predictiveness that is not attained by most other types of individual-models. However, detailed, first-principles cost modeling of nuclear reactors and entire nuclear plants is relatively limited. To address this limitation in the availability of detailed, predictive models based on fundamentals, we recently developed a range of cost models, mostly based on first-principles methodologies, to project full lifecycle costs (LCCs) of nuclear power plants (NPPs) based on multiple parallel SM-HTG-pebble bed reactors (PBRs) and SM-SFRs.

Prosser, Jacob H. [Strategic Analysis, Inc., Arlin↗

DeepLynx Ecosystem 2025

Poor data integration and governance continue to plague complex engineering projects, resulting in missed cost, schedule, and performance targets. Departments operate in isolated systems with manual data exchange, creating fragmented information that compounds errors and leads to significant delays and cost overruns. The DeepLynx ecosystem addresses these challenges through an open-source, modular data management platform that transforms fragmented project data into an integrated digital thread. Built on a federated microservice architecture, the ecosystem comprises seven specialized tools centered around DeepLynx Nexus, a unified data catalog with hierarchical organization and graph-based navigation capabilities. The ecosystem includes: DeepLynx Stream for real-time timeseries data ingestion from industrial sources; DeepLynx Ingest for governed data uploads with formal review workflows; DeepLynx Lattice for ontology-based entity and relationship extraction; DeepLynx Run for workflow orchestration and secure AI/ML compute; DeepLynx Visualize for 3D digital twin visualization; and DeepLynx Insight for AI-assisted document analysis with traceable, grounded responses. Deployable in cloud, on-premise, or hybrid environments using containerized Docker applications and Helm charts, the DeepLynx ecosystem provides flexible infrastructure that adapts to organizational requirements. By consolidating project data into a unified data lake with role-based access controls and OAuth2 authentication, DeepLynx enables digital thread and digital twin capabilities that improve decision-making, reduce risk, and support complex engineering workflows throughout the project lifecycle.

42 - ENGINEERING↗

Data Centers and Digital Assurance Introduction to Supply Chain and Cybersecurity for Data Centers, Session 1

The first session of the TADA (Technical Assistance for Digital Assurance) Data Centers Cohort Workshop, held on October 30, 2025, introduced foundational concepts of Digital Assurance in the context of data center and grid integration. Sponsored by the U.S. Department of Energy, the workshop brought together utilities, data center operators, developers, and vendors to address cybersecurity and supply chain vulnerabilities. The session emphasized the growing criticality of data centers within the electric grid and the need for secure, real-time, bidirectional communication. Participants explored the principles of Digital Assurance, including cybersecurity, cyber-informed engineering (CIE), and lifecycle security, and applied a threat-vulnerability-consequence framework to identify and mitigate risks at the data center–grid interface. Discussions covered a range of threats such as spoofed dispatch signals and insider threats, architectural vulnerabilities like SCADA interfaces and insecure protocols, and potential consequences including cascading grid failures. The session also raised strategic questions about business value, vendor assurance, and defining cyber boundaries and responsibilities. This foundational workshop set the stage for deeper technical analysis and the development of actionable frameworks in subsequent sessions. Session 1 of 3.

24 - POWER TRANSMISSION AND DISTRIBUTION↗

Moving from Information Assurance to Functional Assurance with Engineered Controls

Cyber threats to operational technology demand more than traditional IT defenses—they require full-spectrum mission assurance. Cyber-Informed Engineering (CIE) is an approach that embeds engineered controls into system design to ensure critical functions remain safe and reliable, even under attack. Unlike conventional cybersecurity tools, engineered controls act directly on physical processes to prevent unacceptable outcomes such as equipment damage or mission failure. This session will outline the CIE framework and share examples of consequence-based design that deliver true resilience, not just fail-safe behaviors. Attendees will learn how to integrate these principles into the engineering lifecycle to support resilient-by-design architectures and inform emerging standards. This talk sets the stage for the panel discussion on advancing CIE across sectors as digital and physical systems converge.

24 - POWER TRANSMISSION AND DISTRIBUTION↗

Advanced Test Reactor Safety Basis Update for Gas-Cooled Experiments

The Advanced Test Reactor (ATR) supports neutron irradiation of several types of experiments. One such experiment type is referred to as a gas leadout. Gas leadout experiments actively flow gas through the experiment which allows for active temperature control. It also allows for in-situ data of the experiment. For example, fission gas migration through a fuel sample can be monitored via activity of the sweep gas. Historically, ex-pile equipment and fission product monitors were housed in shielded ATR cubicles. Due to other facility updates, cubicle space is no longer available for gas leadout experiment equipment. To support continued operation of gas leadout experiments, ATR completed a safety basis update that supports a new housing for leadout equipment that may process potentially contaminated gas. In addition to the structure and associated equipment, technical safety requirements regarding handling and storage of experiments needed to be revised to support fueled gas leadout experiments and associated outage configurations. The safety basis update addressed the full lifecycle of these experiments, including experiment movement and interim storage, and credible abnormal events such as failures or leaks in contaminated gas tubing in occupied areas. This paper discusses the completed analyses performed to support the safety basis update associated with gas leadout experiments, including thermal-hydraulic evaluation, probabilistic analysis, and dose consequence analyses.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

WAVES (Wind Asset Value Estimation System) [SWR-23-81]

The Wind Asset Value Estimation System (WAVES) model is a coupling framework for core NREL techno economic analysis software models to estimate capital expenditures (ORBIT), operational expenditures (WOMBAT), and energy production (FLORIS) for offshore wind power plants. Existing workflows to couple the three models for lifecycle performance and cost estimation require a large amount of manual and error-prone setup to combine both shared inputs and dependent outputs, as such WAVES's primary functionality is to wrap the core logic for running standard modeling workflows to ensure shared settings and entangled results are correctly and efficiently combined every time. SEE ALSO: https://pypi.org/project/WAVES/

Hammond, Robert↗

Upcycling of post-consumer mixed polyolefin feedstock: An economic and technical evaluation

Here, this study reports techno-economic and life cycle analyses to evaluate the economic and environmental impacts of mechanically recycled PE/PP blends in the presence of rheology modifiers. Additionally, fiber-reinforced composites derived from the compatibilized blends were prepared and evaluated for their performance compared with virgin plastics. Results suggest that compatibilized PE/PP blends exhibit a 70% lower selling price compared to virgin PE. Furthermore, these blends achieved a 74% reduction in greenhouse gas emissions or climate change impact compared to the virgin counterpart. Fiber-reinforced composites from compatibilized PE/PP blends demonstrated improved or comparable mechanical properties relative to composites made from virgin PE/PP blends. Based on their favorable cost and environmental impact, along with performance comparable to virgin composites, compatibilized PP/PE composites made from post-consumer plastics can find applications in large-scale composite manufacturing.

Fiber reinforced plastics (FRP)↗

A cradle-to-grave life-cycle-assessment of dry-processed Li-ion batteries for electric vehicles

Maxwell-type dry processing has emerged as a promising manufacturing technology for high-areal-loading Li-ion battery electrodes, offering a significant advantage by eliminating the use of the toxic and costly NMP solvent. This study developed a cradle-to-grave life-cycle-assessment model to evaluate the environmental impacts of this innovative technology, benchmarking the results against the conventional NMP-based technique for a configured 42 kWh NMC622-graphite battery pack. Furthermore, the findings reveal that the dry-processing method merits a 4.8% lower energy consumption and achieves environmental impacts up to 47.5% lower in 12 out of 13 categories, highlighting its environmental benefits compared with the NMP-based manufacturing processes.

Electrode↗

Evaluation of Performance Variables to Accelerate the Deployment of Sustainable Aviation Fuels at a Regional Scale

An increase in jet fuel consumption and its associated emissions across the world have led to the need for alternative technologies to produce sustainable aviation fuels (SAF). One option to produce SAFs is to utilize waste or biomass-based feedstocks that has the potential to reduce greenhouse gas emissions by 50% or more compared to conventional jet fuel. However, there is a lack of understanding of how the synergistic effects of key performance variables could hinder or help the deployment of aviation fuels on a regional scale. Here, we assess the implications of key variables-including type and quantity of waste/biomass feedstock availability near the airport, cost of SAF production, life cycle greenhouse gas (GHG) emissions, policies, and fuel/infrastructure logistics-on the deployment of SAF at Chicago's O'Hare International Airport. We consider three ASTM International-approved SAF technologies (Hydroprocessed Esters and Fatty Acids, Fischer-Tropsch, and Alcohol to Jet) that can be blended up to 50% with petroleum-based jet fuel. Results from our analysis show that woody biomass-based Fischer-Tropsch technology has the lowest fuel production costs ($2.31-$2.81/gallon gasoline equivalent) of all pathways, and it reduces life cycle GHG emissions by 86% compared to conventional jet fuel despite the higher availability of crop residues compared to either woody biomass or fats, oils, and greases. Also, infrastructure is available at O'Hare International Airport to blend SAF with Jet A fuel through three terminals directly connected to the airport via pipelines. Our sensitivity analysis shows renewable fuel incentives and feedstock price to be key performance variables affecting the production cost and deployment of SAF.

alcohol-to-jet↗

PV in the circular economy, a dynamic framework analyzing technology evolution and reliability impacts

Rapid, terawatt-scale deployment of photovoltaic (PV) modules is required to decarbonize the energy sector. Despite efficiency and manufacturing improvements, material demand will increase, eventually resulting in waste as deployed modules reach end of life. Circular choices for decommissioned modules could reduce waste and offset virgin materials. We present PV ICE, an open-source python framework using modern reliability data, which tracks module material flows throughout PV life cycles. We provide dynamic baselines capturing PV module and material evolution. PV ICE includes multimodal end of life, circular pathways, and manufacturing losses. We present a validation of the framework and a sensitivity analysis. Results show that manufacturing efficiencies strongly affect material demand, representing >20% of the 9 million tons of waste cumulatively expected by 2050. Reliability and circular pathways represent the best opportunities to reduce waste by 56% while maintaining installed capacity. Shorter-lived modules generate 81% more waste and reduce 2050 capacity by 6%.

14 SOLAR ENERGY↗

Life cycle greenhouse gas emissions and energy use of polylactic acid, bio-derived polyethylene, and fossil-derived polyethylene

Bioplastics recently have become an attractive, viable, and popular alternative to conventional petroleum-based plastics, with the hope that replacing fossil-derived plastics with renewable alternatives will reduce greenhouse gas (GHG) emissions and fossil energy consumption (FEC). The bioplastic industry is encouraging creative designs and enhanced properties such as biodegradability, which is considered a sustainable solution for waste plastic management. However, biodegradability also means that carbon in the product is emitted to the atmosphere as GHG emissions. In this paper, a life cycle analysis (LCA) of biodegradable polylactic acid (PLA) and bio-polyethylene (bio-PE) plastics was conducted to understand the environmental effects of these bioplastics from feedstock production to product end-of-life (EOL). In particular, emissions from biodegradability (EOL emissions) are accounted for. The results were compared to those of conventional fossil-based plastics such as high-density polyethylene (HDPE) and low-density polyethylene (LDPE). Results showed that the lowest GHG emissions (-1.0 and 1.7 kg CO 2 e per kg for bio-PE and PLA with no biodegradation, respectively) and FEC (29 and 46 MJ per kg of bio-PE and PLA, respectively) were achieved with bio-derived plastics, particularly bio-PE plastic. However, despite the benefits of biogenic carbon uptake, when landfill and composting emissions were considered for the PLA pathway, the life cycle emissions of PLA increase significantly, from 16% to 163% depending on the biodegradation condition, compared to the case where there is no degradation in the landfill. This study also contributed to understand the effects on the GHG emissions of biodegradability in landfill and composting scenarios, regional electricity mix, and plastics manufacturing technologies. (C) 2020 The Authors. Published by Elsevier Ltd.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Multifunctional landscapes for dedicated bioenergy crops lead to low-carbon market-competitive biofuels

Switchgrass is a promising feedstock for cellulosic biorefineries, due to its ability to maintain comparatively high biomass yields across a wide range of soil and climatic conditions. However, there is an incomplete understanding of the economic and environmental tradeoffs associated with cultivating switchgrass on low-productivity land for conversion to biofuels. This study surveys prior literature and demonstrates a new integrated assessment framework, including agroecosystem, ecosystem services valuation, technoeconomic, and life-cycle assessment models, to quantify and contextualize the economic and environmental impacts of switchgrass cultivation on marginal land with downstream conversion to biofuels. Monetizing and incorporating the value of ecosystem services, such as improved water quality benefits from nitrate and sediment reductions, climate change mitigation benefits from CO 2 emission reduction, and recreational and pollination benefits from increased biodiversity, the modeled multifunctional landscape reduces the ethanol production cost by 33.3–58.9 cents/$\scriptsize{L}$-gasoline-equivalent ($\$$1.3–2.2/gge). Planting switchgrass in low productivity land improves soil health, resulting in the carbon footprint reduction credit of 12.8–20.2 gCO 2e /MJ. For an improved switchgrass-to-ethanol conversion pathway with the maximum benefits from ecosystem services, the minimum ethanol selling price and carbon footprint of ethanol, respectively, could reach to 31 cents/$\scriptsize{L}$-gasoline-equivalent (47% reduction relative to average gasoline price) and 3 gCO 2e /MJ (97% reduction relative to gasoline). In conclusion, this low carbon renewable ethanol leads to substantial State and/or Federal policy incentives (~$\$$1/$\scriptsize{L}$-gasoline-equivalent) providing a large benefit to biorefinery operators, farmers, and the public as a whole.

09 BIOMASS FUELS↗

Machine Learning Reduced Order Model for Cost and Emission Assessment of a Pyrolysis System

Biomass pyrolysis is a promising approach for producing economic and environmentally-friendly fuels and bioproducts. Biomass pyrolysis experiments show that feedstock properties have a significant impact on product yields and composition. Scientists are developing detailed chemical reaction mechanisms to capture the relationships between biomass composition and pyrolysis yields. These mechanisms can be computationally intensive. In this study, we investigate the use of a machine learning reduced order model (ROM) for assessing the costs and emissions of a pyrolysis biorefinery. Here, we developed a Kriging-based ROM to predict pyrolysis yields of 314 feedstock samples based on the results of a detailed chemical kinetic pyrolysis mechanism. The ROM is integrated into a chemical process model for calculating mass and energy yields in a commercial-scale (2000 tonne/day) biorefinery. The ROM estimated biofuel yields of 65 to 130 gallons per ton of dry biomass. This results in biofuel minimum fuel-selling prices of $2.62 to $5.43 per gallon and emissions of -13.62 to 145 kg of CO 2 per MJ. The ROM achieved an average mean square error of 1.8e-20 and a mean absolute error of 0.53%. These results suggest that ROMs can facilitate rapid feedstock screening for biorefinery systems.

09 BIOMASS FUELS↗